Bone Quality Impact On Surgical Guide Accuracy Via Machine Learning

Using machine learning, this retrospective study examines the effect of alveolar bone quality on CAD/CAM surgical guide precision in order to optimise the design of optimal placement in a variety of skeletal conditions. A retrospective study analyzed single-tooth implant cases at our hospital (Dec 2021–Feb 2023). Preoperative implant planning and fully guided digital templates were designed using 3D implant studio software. Postoperative CBCT reconstructed actual implant positions, with orthogonal validation projecting deviations onto buccolingual and mesiodistal planes. The classification of the bone density was done using Misch criteria, and the results were compared before the operation. The placement of the implant (cervical/apical positions in the buccolingual, medial, and vertical) was classified into 6 categories. The random-forest model assessed the factors influencing the precision of the guidance, with emphasis on the effect of the bone quality on the location of the location of the implant. Following inclusion/exclusion criteria, 35 patients received full-guided surgery. Excluding 3 cases, initial stability was ≥20 N·cm. Cervical/apical/angular deviations averaged 0.94±0.59 mm, 1.40±0.81mm, and 4.10°±2.99°, respectively. Random forest analysis revealed bone quality (Misch D3/D5 distribution) as the dominant factor: higher D3 correlated with smaller deviations, while D5 increased errors. Bone influence varied regionally, demonstrating complex biomechanical interactions. For patients with varying bone quality, it is possible to improve the proportion of Misch D3 bone around implants through digital guides in order to improve the initial implant stability.